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A New Analysis of Iterative Refinement and Its Application to Accurate Solution of Ill-Conditioned Sparse Linear Systems Carson A New Analysis of Iterative Refinement and its Application to Accurate Solution of Ill-Conditioned Sparse Linear Systems Carson, Erin and Higham, Nicholas J. 2017 MIMS EPrint: 2017.12 Manchester Institute for Mathematical Sciences School of Mathematics The University of Manchester Reports available from: http://eprints.maths.manchester.ac.uk/ And by contacting: The MIMS Secretary School of Mathematics The University of Manchester Manchester, M13 9PL, UK ISSN 1749-9097 A NEW ANALYSIS OF ITERATIVE REFINEMENT AND ITS APPLICATION TO ACCURATE SOLUTION OF ILL-CONDITIONED SPARSE LINEAR SYSTEMS∗ ERIN CARSONy AND NICHOLAS J. HIGHAMz Abstract. Iterative refinement is a long-standing technique for improving the accuracy of a computed solution to a nonsingular linear system Ax = b obtained via LU factorization. It makes use of residuals computed in extra precision, typically at twice the working precision, and existing results guarantee convergence if the matrix A has condition number safely less than the reciprocal of the unit roundoff, u. We identify a mechanism that allows iterative refinement to produce solutions with normwise relative error of order u to systems with condition numbers of order u−1 or larger, provided that the update equation is solved with a relative error sufficiently less than 1. A new rounding error analysis is given and its implications are analyzed. Building on the analysis, we develop a GMRES-based iterative refinement method (GMRES-IR) that makes use of the computed LU factors as preconditioners. GMRES-IR exploits the fact that even if A is extremely ill conditioned the LU factors contain enough information that preconditioning can greatly reduce the condition number of A. Our rounding error analysis and numerical experiments show that GMRES-IR can succeed where standard refinement fails, and that it can provide accurate solutions to systems with condition numbers of order u−1 and greater. Indeed in our experiments with such matrices|both random and from the University of Florida Sparse Matrix Collection|GMRES-IR yields a normwise relative error of order u in at most 3 steps in every case. 1. Introduction. Ill-conditioned linear systems Ax = b arise in a wide variety of science and engineering applications, ranging from geomechanical problems [11] to computational number theory [4]. When A is ill conditioned the solution x to Ax = b is extremely sensitive to changes in A and b. Indeed, when the condition number κ(A) = kAkkA−1k is of order u−1, where u is the unit roundoff, we cannot expect any accurate digits in a solution computed by standard techniques. This poses an obstacle in applications where an accurate solution is required, of which there is a growing number [3], [13], [21], [22], [32]. Iterative refinement (Algorithm 1.1) is frequently used to obtain an accurate so- lution to a linear system Ax = b. Typically, one computes an initial approximate solution xb using Gaussian elimination (GE), saving the factorization A = LU. Here and throughout, to simplify the notation we assume that A is a nonsingular matrix for which any required row or column interchanges have been carried out in advance (that is, \A ≡ P AQ", where P and Q are appropriate permutation matrices). After 1 2 computing the residual r = b − Axb in higher precision u, where typically u = u , one reuses L and U to solve the system Ad = r, rewritten as LUd = r, by substi- tution. The original approximate solution is then refined by adding the corrective term, xb xb + d. This process is repeated until a desired backward or forward error criterion is satisfied. If A is very ill conditioned however, the iterative refinement process may not ∗Version of March 27, 2017. Funding: The work of the second author was supported by Math- Works, European Research Council Advanced Grant MATFUN (267526), and Engineering and Phys- ical Sciences Research Council grant EP/I01912X/1. The opinions and views expressed in this pub- lication are those of the authors, and not necessarily those of the funding bodies. yCourant Institute of Mathematical Sciences, New York University, New York, NY. (er- [email protected], http://math.nyu.edu/~erinc). zSchool of Mathematics, The University of Manchester, Manchester, M13 9PL, UK ([email protected], http://www.maths.manchester.ac.uk/~higham). 1We are not concerned here with iterative refinement in fixed precision, which can also benefit accuracy, though to a lesser extent [15, Chap. 12], [33]. 1 Algorithm 1.1 Iterative Refinement Input: n × n matrix A; right-hand side b; maximum number of refinement steps imax. Output: Approximate solution xb to Ax = b. 1: Compute LU factorization A = LU. 2: Solve Ax0 = b by substitution. 3: for i = 0 : imax − 1 do 4: Compute ri = b − Axi in precision u; store in precision u. 5: Solve Adi = ri. 6: Compute xi+1 = xi + di. 7: if converged then return xi+1, quit, end if 8: end for 9: % Iteration has not converged. invhilb 1020 1015 1010 κ Ub −1Lb−1A 105 ∞( ) 1 + κ∞(A)u 100 10 15 20 25 randsvd 105 104 103 102 b −1 b−1 κ∞(U L A) 1 10 1 + κ∞(A)u 100 10 15 20 25 −1 −1 Fig. 1.1. Comparison of κ1(Ub Lb A) and 1+κ1(A)u for a computed LU factorization with partial pivoting, for matrices of dimension shown on the x-axis. These matrices are very ill condi- 13 35 tioned with κ1(A) ranging from 10 to 10 . Computations were in double precision arithmetic. Top: inverse Hilbert matrix. Bottom: matrices generated as gallery('randsvd',n,10^(n+5)) in MATLAB. converge, and indeed all existing results on the convergence of iterative refinement require A to be safely less than u−1. Nevertheless, despite the ill conditioning of A, there is still useful information contained in the LU factors and their inverses (perhaps implicitly applied). It has been observed that if Lb and Ub are the computed factors of A from LU factorization with partial pivoting then κ(Lb−1AUb−1) ≈ 1 + κ(A)u even for κ(A) u−1, where Lb−1AUb−1 is computed by substitution; for discussion and further experiments see [24], [28], [29]. This approximation can also be made in the case where left-preconditioned is used, that is, κ(Ub−1Lb−1A) ≈ 1 + κ(A)u. The experimental results shown in Figure 1.1 illustrate the quality of this approximation. The results in Figure 1.1 lead us to question the conventional wisdom that iterative refinement cannot work in the regime where κ(A) > u−1. If the LU factors contain useful information in that regime, can iterative refinement be made to work there 2 too? We will give a new rounding error analysis that identifies a mechanism by which iterative refinement can indeed work when κ(A) > u−1, provided that we can solve the equations for the updates on line5 of Algorithm 1.1 with some relative accuracy. We will then use the analysis to develop an implementation of Algorithm 1.1 that enables accurate solution of sparse, very ill conditioned systems. We use the generalized minimal residual (GMRES) method [31] preconditioned by the computed LU factors to solve for the corrections. We refer to this method as GMRES-based iterative refinement (GMRES-IR). −1 We show that in the case where the condition number κ1(A) is around u , our approach can obtain a solution xb to Ax = b for which the forward error kxb−xk1=kxk1 is of order u. Extra precision (assumed to be with a unit roundoff of u2) need only be used in computing the residual, in the triangular solves involved in applying the preconditioner, and in matrix-vector multiplication with A. An advantage of our approach is that it can succeed when standard iterative re- finement (using the LU factors to solve Ad = r) fails or is, in the words of Ma et al. [22], \on the brink of failure". Ma et al. [22] need to solve linear programming prob- lems arising in a biological application and their attempts to use iterative refinement in the underlying linear system solutions were only partially successful. GMRES-IR offers the potential for better results in this application. We note that there is growing interest in using lower precisions such as single or even half precision in climate and weather modeling [27] and machine learning [12], but this brings an increased likelihood that the problems encountered will be very ill conditioned relative to the working precision. Our work, which is applicable for any u, could be especially relevant in these contexts. In Section2 we present the new rounding error analysis and investigate its impli- cations. In Section3 we explain how we use preconditioned GMRES within iterative refinement and give, using the results of Section2, theoretical justification that this GMRES-based iterative refinement can yield an error of the order of u even in cases −1 where κ1(A) ≥ u . Since we are primarily concerned with sparse linear systems, we discuss in Section 3.1 various choices of pivoting strategy and how these affect the nu- merical behavior. In Section4 we discuss other, related work on iterative refinement. Numerical experiments presented in Section5 confirm our theoretical analysis. Our numerical experiments motivate a two-stage iterative refinement approach, which we briefly discuss in Section 5.3, that first attempts the less expensive standard iterative refinement and switches to a GMRES-IR stage in the case of slow convergence or divergence. 2. Error analysis of iterative refinement. The most general rounding error analysis of iterative refinement is that of Higham [15, Sec. 12.1], which appeared first in [14].
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